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Women in Data Science (WiDS) Conference, 2017

Fri, Feb 3, 2017

Rutgers University-Newark

Women in Data Science (WiDS) Conference promotes and motivates women in the field of Data Science, providing a platform to present their cutting-edge research. It also aims to strengthen the academe- industrial ties.The one-day technical conference will provide opportunity to hear about the latest data science related research in a number of domains, learn how leading-edge companies are leveraging data science for success, and connect with potential mentors, collaborators, and others in the field.

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CHMPR Technical and IAB 2017 Semi-Annual Meeting

Wed, May 31, 2017

Rutgers University-Newark

Semi-annual Industrial Advisory Board (IAB) Meetings are an opportunity for face-to-face collaborations among CHMPR members. Industry members, research faculty, and students review the progress of current projects and the potential of proposed projects; discuss the performance of CHMPR relating to research areas, test beds, education, and technology transfer; and investigate prospective research topics to address current and future industry needs.

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Rutgers University-Newark Research Computing Fair

Mon, Nov 6, 2017
Rutgers University-Newark 

11:30AM – 11:45AM A word with Dr.Nabil Adam

11:45AM – 12:00PM Discussion with Rutgers CIO Michele Norin

12:00PM – 01:30PM Lunch,poster session, tour of Data center (Englehard Hall)

01:30PM – 02:00PM Hackathon by John Cain & Amarel Experts

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NSF Conference on Practice and Science of Public Participation in STEM Research on Data Enabled Science and Engineering

Thu, Feb 8, 2018

Rutgers University-Newark

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Sun, Jun 19, 2016 to Thu, Jun 23, 2016

Rutgers University, Newark

“Big Libraries, Big Data, Big Innovation”.

The Joint Conference on Digital Libraries (JCDL) is a major international forum focusing on digital libraries and associated technical, practical, and social issues. Participation is sought from all parts of the world and from the full range of disciplines and professions involved in digital library research and practice, including computer science, information science, librarianship, archival science and practice, museum studies and practice, technology, medicine, social sciences, and humanities. All domains – academics, government, industry, and others – are encouraged to participate as presenters or attendees.



This is Data Science! Speaker series

Fri, Feb 19, 2016 – 9:52pm to Fri, May 6, 2016 – 9:52pm

Aidekman Research Center (CMBN)

Rutgers is happy to announce the Data Science speaker series and line-up for the spring 2016. As you will see, the speakers span education, text analysis, computational neuroscience and from rising stars to established leaders in their fields. The goal is to show off the many ways in which data science is affecting diverse areas of scholarship. Talks are all scheduled for Fridays at 2:30 in Aidekman’s first floor auditorium. I look forward to seeing you there!



2/5/2016 – Ryan Baker, Associate Professor of Cognitive Studies in Education, Columbia University Teachers College. Research area: Educational data mining. 371A Smith Hall

2/19/2016 – Eugene Wu, Assistant Professor of Computer Science, Columbia University. Research area: Visualization and databases

3/25/2016 – Brenden Lake, Moore-Sloan Data Science Fellow Center for Data Science, NYU, Research area: Computational models of human and machine cognition

4/8/2016 – Stephan Mandt, Postdoctoral Researcher in the Institute for Data Sciences and Engineering, Columbia University. Research area: Statistical machine learning

4/15/2016 – Barbara Engelhardt, Assistant Professor Department of Computer Science, Princeton University. Research area: Statistical models and methods for analysis of high-dimensional data and biological mechanisms of complex phenotypes and human diseases.

4/22/2016 – Mark Finlayson, Assistant Professor of Computer and Information Sciences, Florida International University. Research area: Computational models of narrative

5/6/2016 – Nathaniel Daw, Professor of Princeton Neuroscience Institute and Department of Psychology, Princeton University. Research area: Reinforcement learning from computational, neural, and behavioral perspectives


NSF Workshop 2016

Thu, Jan 7, 2016 to Fri, Jan 8, 2016

NY Academy of Sciences

“Data Science, Learning, and Applications to Biomedical & Health Sciences (DSLA-BHS2016)”
SPONSORED BY: Rutgers I-DSLA, the NSF Smart and Connected Health Program, the NSF North East Big Data Innovation Hub and in Partnership with the NYAS

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Connecting natural and artificial neural networks with functional brain imaging

Fri, May 15, 2015

Center for Law and Justice
CLJ 123 Washington Street ROOM 572

Abstract: Functional neuroimaging and neural network modeling were both introduced to cognitive science in the 1980s, and have both produced influential research. Yet surprisingly, the programs have advanced with little mutual influence. I will describe two different approaches to more directly connecting cognitive neural network models with functional brain imaging in the search for the neural bases of cognition. In the first, direct estimates of gross neural connectivity in real brains are used to shape the architecture of an artificial neural network (ANN).

Timothy Rogers

Simulations with the resulting model are then used to understand patterns of healthy and impaired behaviors, as well as patterns of functional activation revealed by standard univariate contrast methods.

We have applied this approach successfully to develop network models of single-word processing and of semantic cognition that provide a unified account of key phenomena in these domains. Yet this approach also ignores an important contribution of neural network modeling, the possibility that neural representations can be instantiated as highly distributed patterns of activation that are strongly shaped by learning.

In the second approach, I consider how statistical analysis of brain imaging data might best proceed if real neural networks have the properties predicted by neural network models. In analyses of synthetic data generated by such models, we have shown that common univariate and even popular multivariate approaches adopt implicit assumptions that prevent them from discovering essential representational structure. I will describe some new sparsity-based optimization methods that, because they begin by assuming that representations might be distributed, are better able to find such representations where they exist in the data.

When applied to real fMRI data, these methods offer a quite different picture of the nature of neuro-cognitive representations than those yielded by standard approaches. Together the work suggests that artificial neural network models can provide a useful conceptual bridge for connecting cognition and neuroscience, and that the joint application of these methods may lead to new insights about the neural bases of cognition not achievable by other means.

Novel Tools in Computational Chemistry Coding workshop (NTC3)

Sat, Apr 11, 2015

Paul Robeson Campus Center
Bergen Room

Computer hardware/software development, and computational chemistry codes originate in separate scientific communities, i.e., computer science/electrical engineering on one side, and chemistry/physics on the other side. Nowadays, the birth of novel hardware solutions, such as GPUs and coprocessors (e.g., Xeon Phi), demands a shift in the common coding paradigms in chemistry.


The NTC3 workshop is designed to promote cross-disciplinary interactions between chemistry and computer science and to set the stage for new collaborations.

The specific aim of NTC3 is to bring together chemistry and computer science experts with the goal of bringing both disciplines up-to-speed with the latest developments and main difficulties of both disciplines.

More Info :Michele Pavanello


Decision Models of Medical Signal and Imaging Data to Improve Medical Diagnoses

Thu, Jan 15, 2015

Aidekman Research Center (CMBN)
Aidekman seminar room

The overarching goal of our research is to develop new data analytics techniques based on applied optimization and machine learning. The main driving application of our techniques is to assist physicians in recognizing abnormality patterns (and/or patterns of interest) in medical signal and imaging data.



The main focus of our work is on feature selection, which has become an emerging problem in machine learning and optimization. Searching for the optimal set of features in decision models is computationally challenging, and it also needs to avoid model overfitting. While the main objective of most decision models is to provide an accurate decision or prediction outcome, physical/physiological interpretation of such models are extremely important in medical domain.

Our group has developed a host of feature selection techniques that can improve the accuracy and interpretability of our medical decision models. In this talk, I will discuss a few real life medical applications of our techniques, which span from prediction of neural response to visual stimuli from functional magnetic resonance imaging (fMRI), diagnostic classification of attention-deficit/hyperactivity disorder from structural (MRI), and treatment planning of lung cancer using PET/CT. If time permits, I will give an overview of other research projects undertaken in our group.

Dr. Nabil R. Adam


Probabilistic modeling for data science and science: Nonparametric Bayes and beyond

Wed, Jan 14, 2015

Aidekman Research Center (CMBN)
Aidekman seminar room

High-dimensional datasets containing data of multiple data types have become commonplace across business, health, and science. In theory, these rich data sets support fine-grained inferences; however, the data analysis problem becomes more difficult as the data become more varied, noisy, and sparsely observed.


Moreover, people with the skills to navigate the minefield of restrictive assumptions and special-purpose analyses are scarce and expensive to train. I will describe CrossCat and BayesDB, two projects that aim to alleviate these problems. CrossCat uses nonparametric Bayesian methods from machine learning to offer generic inference for tabular data. BayesDB offers a simple language for asking questions of tabular data.

I will conclude by 1) highlighting connections to my broader research program, 2) applications of nonparametric Bayesian approaches in a variety of fields including neuroscience and geography, 3) the increasing importance of probabilistic modeling, and 4) the importance of data science skills in business, health, and science.

Patrick Shafto

Dr. Nabil R. Adam


Computation and Critical Dynamics in the Brain: Normal Function and Pathology

Mon, Jan 12, 2015

Aidekman Research Center (CMBN)
Aidekman seminar room

To a computer scientist, the brain is a very strange device: build of odd materials with narrow safety margins, possessed of various features our own computers are only gradually approaching, and exhibiting remarkable performance. After a review of the brain as an information processing device (its materials, speed, and performance) we proceed to discuss two hypothesized operating principles.


One, the optimality hypothesis, dominates our understanding of neuronal information processing: it is widely appreciated that the brain typically operates near theoretical limits, not only in task performance but also in adaptivity and hardware utilization.

We proceed to discuss challenges to the optimality hypothesis, and show that some troublesome phenomena can be accounted for by a second principle: the criticality hypothesis, which posits that that the brain operates in a dangerous near-critical dynamic regime; that it devotes substantial resources to avoiding super-criticality; and that various pathologies result from the criticality boundary being crossed. Phenomena discussed include epilepsy, sleep, Parkinson’s disease, and non-psychotic hallucinations (such as tinnitus, Charles Bonnet syndrome, and phantom limb) which follow diminution of sensory input.

Barak Pearlmutter

Dr. Nabil R. Adam